In the area of aerial reconnaissance, there is a requirement to be able to ascertain the presence of individuals in the presence of clutter for instance both in major urban settings in which the presence of an individual can be confused with other objects in the urban environment, as well as in rural environments in which for instance the presence of an individual can be confused with terrain features. Surveillance of such areas to determine the presence of individuals is often times referred to as dismount detection, which refers to the detection of for instance military personnel leaving a vehicle. This definition has been expanded to include the ability to locate humans for instance as they emerge from buildings and the like and to distinguish them from other animals.
The problem with the aerial surveillance is that when for instance done at high-altitude, the images obtainable at such altitudes are very blurry and composed of very few pixels. The problem therefore is to be able to analyze such blurry objects to ascertain whether or not the blurred object is a result of human activity. Moreover, how to detect low resolution moving objects at high altitudes with for instance a single snapshot for a whole city is difficult.
The purpose of the dismount is to obtain the x, y location of a person in an image. The challenge is to analyze the characteristics of the pixels in the image for instance to ascertain if the object represented by the pixels is moving at a speed having a characteristic motion profile indicative of a human being. Other indications of the presence of human beings are whether the thermal signature is like that emitted by a human being. It is also important to distinguish objects of interest from the background. It is noted that a moving object operates in a variety of conditions and that thermal imaging does not necessarily work to indicate the existence of a dismount. Moreover, different lighting conditions involved in daylight or nighttime surveillance operations and the presence of clouds make thermal signatures weak since the dismounts are not easily separated from background. While bright sunlight can pick up targets, a single threshold does not necessarily work for all lighting conditions.
Thus, dismount detection is a very challenging problem due to the presence of clutter such as similar objects in the background, low pixel-density of dismounts involving 2-30 pixels in size, signal-to-noise ratios, low-contrast difference between dismounts and background, weak thermal signatures, changing perspective of dismounts due to various camera viewing points, camera ego-motion due to the mobile setup when for instance UAVs are involved, parallax, occlusions and significant changes in illumination such as occasioned by day, night and cast shadow conditions. As a result, present methods yield less than 50% detection rates. There is therefore a need to improve on these relatively poor detection rates.
Thus, a heretofore unaddressed need exists in the industry to address the aforementioned deficiencies and inadequacies.